我的檔案路徑可以用window寫法嗎

[henrychou1233]

data:
DA_batch_size: 30
batch_size: 30
category: grid
data_dir: /mnt/d/download2/aaa/Dynamic-noise-AD-master/MVTec/
image_size: 256
imput_channel: 4
manualseed: -1
mask: true
name: MVTec
metrics:
image_level_AUROC: true
image_level_F1Score: true
pixel_level_AUROC: true
pixel_level_F1Score: true
pro: true
threshold:
manual_image: null
manual_pixel: null
method: adaptive
model:
DA_epochs: 1 # nr. of fine tune epochs for fe
DA_fine_tune: 1
DA_learning_rate: 1e-4
DA_rnd_step: true # pick noising level for DA according to uniform distribution
dynamic_steps: true # Dynamic implicit conditioning
KNN_metric: l1
anomap_excluded_layers: # excluded feature layers for anomaly map creation

• 0
  anomap_weighting: 0.85 # weight for latent anomaly map
  attn_reso:
• 32
• 16
• 8
• 4
  beta_end: 0.0195
  beta_start: 0.0015
  channel_mults:
• 1
• 2
• 2
• 4
• 4
  checkpoint_dir: /mnt/d/download2/aaa/Dynamic-noise-AD-master/checkpoints/MVTec/
  checkpoint_epochs: 300
  checkpoint_name: weights
  consistency_decoder: 0 # consistency decoder for better image quality at the cost of additional runtime
  device: cuda
  distance_metric_eval: combined
  downscale_first: 1 # noiseless scaling
  ema: true
  ema_rate: 0.999
  epochs: 9
  eta: 0 # 0 corresponds to DDIM sampling and 1 to DDPM
  eta2: 4 # DDAD conditioning
  exp_name: default
  fe_backbone: resnet34
  head_channel: -1
  knn_k: 20
  latent: true
  latent_backbone: VAE
  latent_size: 32
  learning_rate: 1e-4
  multi_gpu: false
  n_head: 8
  noise: Gaussian
  noise_sampling: 0 # noise image or not
  num_workers: 30
  optimizer: AdamW
  save_model: true
  schedule: linear
  seed: 42
  selected_features: # selected layer for KNN search
• 1
  skip: 8 # steps to skip during inference
  skip_DA: 8 # steps to skip during domain adaptation
  test_trajectoy_steps: 80 # maximum noising level
  test_trajectoy_steps_DA: 80 # maximum noising level for domain adaptation
  trajectory_steps: 1000
  unet_channel: 192
  visual_all: true # additional visual output of heatmaps
  weight_decay: 0.01
  以上是我的config包

但都一直偵測不到
(AI) PS D:\download2\aaa\Dynamic-noise-AD-master> python main.py
2024-07-15 10:15:07.106301: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-07-15 10:15:07.624565: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
Num params: 281088004
Current device is cuda
Traceback (most recent call last):
File "D:\download2\aaa\Dynamic-noise-AD-master\main.py", line 158, in
execute_main_test()
File "D:\download2\aaa\Dynamic-noise-AD-master\main.py", line 154, in execute_main_test
train(args)
File "D:\download2\aaa\Dynamic-noise-AD-master\main.py", line 74, in train
trainer(unet, constants_dict, ema_helper, config)
File "D:\download2\aaa\Dynamic-noise-AD-master\train.py", line 31, in trainer
trainloader = torch.utils.data.DataLoader(
File "C:\Users\user.conda\envs\AI\lib\site-packages\torch\utils\data\dataloader.py", line 350, in init
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "C:\Users\user.conda\envs\AI\lib\site-packages\torch\utils\data\sampler.py", line 143, in init
raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
ValueError: num_samples should be a positive integer value, but got num_samples=0